More intelligent knowledge graph: A large language model-driven method for knowledge representation in geospatial digital twins

Knowledge graphs (KGs) can describe the nature and relationships of geographic entities and are an essential knowledge base for realizing geospatial digital twins (GDTs). However, existing KGs make it challenging to describe dynamic geographic entities under geographic spatiotemporal evolution accurately. Furthermore, they are constrained by the professional backgrounds of their users, which hinders updates and communication. Therefore, the research constructed an “event-object-state” three-domain associated GDT-oriented KG, proposed a large language model (LLM) −driven KG dynamic update algorithm, and established a KG intelligent Q&A method integrating LLM. We developed a prototype system and selected an earthquake disaster as a typical geographic event for experimental analysis. The results showed that the proposed method can reflect the space, time, state, evolution process, and interrelationships of geographic entities in a more comprehensive way, support users to build, update, and query KGs using natural language, with an updating efficiency of less than 1 min, and an updating quality comparable to that of manual updating by experts. Compared with the traditional KGs, our method can represent virtual geographic entities and has significant advantages in intelligence and automation, which effectively breaks down professional barriers and supports the construction of GDTs with the need for rapid updating of knowledge.